In this module, you will understand what is R language, business analytics with R, Installing R and much more…

Understand Business Analytics and R
Knowledge on the R language
Community and ecosystem
Understand the use of ‘R’ in the industry
Compare R with other software in analytics
Install R and the packages useful for the course
Perform basic operations in R using command line
Learn the use of IDE R Studio and Various GUI
Use the ‘R help’ feature in R
Knowledge about the worldwide R community collaboration.

R language is widely used among statisticians and data miners for developing statistical software and data analysis.

The various kinds of data types in R and its appropriate uses
The built-in functions in R like: seq(), cbind (), rbind(), merge()
Knowledge on the various Sub-setting methods in R
Summarize data by using functions like: str(), class(), length(), nrow(), ncol() in R
Use of functions like head(), tail() for inspecting data
Indulge in a class activity to summarize data in R

One of the most important aspects of computing with data is the ability to manipulate it, to enable subsequent analysis and visualization. R offers a wide range of tools for this purpose.

The various steps involved in R for Data Cleaning
Functions used in R for Data Inspection
Tackling the problems faced during Data Cleaning
Uses of the functions like grep(), sub()
Coerce the data in R
Uses of the apply() functions.

This module describes how to enter or import data into R, and how to prepare it for use in statistical analyses.

Import data from spreadsheets and text files into R
Import data from other statistical formats like sas7bdat and spss into R
Package installation used for database import
Connect to RDBMS from R using ODBC and basic SQL queries in R
Basics of Web Scraping in R

Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set.

Understanding the Exploratory Data Analysis (EDA)
Implementation of EDA on various datasets in R
Box plots
Understanding the cor() in R
EDA functions like summarize()
llist()
Multiple packages in R for data analysis
The Fancy plots like Segment plot and HC plot in R.

One of the most appealing things about R is its ability to create data visualizations with just a couple of lines of code.

Understanding on Data Visualization
Graphical functions present in R
Plot various graphs in R like table plot, histogram, box plot
Customizing Graphical Parameters to improvise the plots
Understanding GUIs like Deducer and R Commander
Introduction to Spatial Analysis in R

This module will concentrate on k means clustering techniques.

Introduction to Data Mining in R
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms in R
K-means Clustering.

Understand Association Rule Mining in R and sentiment analysis.

Association Rule Mining in R
Sentiment Analysis.

We’ll demonstrate linear regression and logistic regression in this module

Linear Regression in R
Logistic Regression in R

In this module, learn about Anova and predictive analysis techniques in R

Anova
Predictive Analysis.

This module will enlighten you over the decision trees in R, classification Rules, concepts of random forest and much more..

Decision Trees in R
Algorithm for creating Decision Trees
Greedy Approach: Entropy and Information Gain
Creating a Perfect Decision Tree in R
R Classification Rules for Decision Trees
Concepts of Random Forest in R
Working of Random Forest in R
Features of Random Forest in R

This module discusses the concepts taught throughout the course and their implementation in a Project..

Analyse Census Data
To predict insights on the income of the people based on the factors like : Age, education, work-class, occupation, etc.